Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization

Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization

Background and purpose: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulat...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Amal Joseph Varghese, Varsha Gouthamchand, Balu Krishna Sasidharan, Leonard Wee, Sharief K Sidhique, Julia Priyadarshini Rao, Andre Dekker, Frank Hoebers, Devadhas Devakumar, Aparna Irodi, Timothy Peace Balasingh, Henry Finlay Godson, T Joel, Manu Mathew, Rajesh Gunasingam Isiah, Simon Pradeep Pavamani, Hannah Mary T Thomas
Μορφή: Άρθρο
Γλώσσα:English
Έκδοση: Elsevier 2023-04-01
Σειρά:Physics and Imaging in Radiation Oncology
Θέματα:
Head-and-neck cancer
Radiomics
Loco-regional recurrence
Multi-institutional
Prognosis
Machine learning
Διαθέσιμο Online:http://www.sciencedirect.com/science/article/pii/S2405631623000416

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http://www.sciencedirect.com/science/article/pii/S2405631623000416

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